In this paper we address the task of learning how to segment a particular class of objects, by means of a training set of images and their segmentations. In particular we propose a method to overcome the extremely high training time of a previously proposed solution to this problem, Kernelized Structural Support Vector Machines. We employ a one-class SVM working with joint kernels to robustly learn significant support vectors (representative image-mask pairs) and accordingly weight them to build a suitable energy function for the graph cut framework. We report results obtained on two public datasets and a comparison of training times on different training set sizes.

Learning Graph Cut Energy Functions for Image Segmentation / Manfredi, Marco; Grana, Costantino; Cucchiara, Rita. - ELETTRONICO. - (2014), pp. 960-965. ((Intervento presentato al convegno 22nd International Conference on Pattern Recognition tenutosi a Stockholm, Sweden nel Aug. 24-28 [10.1109/ICPR.2014.175].

Learning Graph Cut Energy Functions for Image Segmentation

MANFREDI, MARCO;GRANA, Costantino;CUCCHIARA, Rita
2014

Abstract

In this paper we address the task of learning how to segment a particular class of objects, by means of a training set of images and their segmentations. In particular we propose a method to overcome the extremely high training time of a previously proposed solution to this problem, Kernelized Structural Support Vector Machines. We employ a one-class SVM working with joint kernels to robustly learn significant support vectors (representative image-mask pairs) and accordingly weight them to build a suitable energy function for the graph cut framework. We report results obtained on two public datasets and a comparison of training times on different training set sizes.
22nd International Conference on Pattern Recognition
Stockholm, Sweden
Aug. 24-28
960
965
Manfredi, Marco; Grana, Costantino; Cucchiara, Rita
Learning Graph Cut Energy Functions for Image Segmentation / Manfredi, Marco; Grana, Costantino; Cucchiara, Rita. - ELETTRONICO. - (2014), pp. 960-965. ((Intervento presentato al convegno 22nd International Conference on Pattern Recognition tenutosi a Stockholm, Sweden nel Aug. 24-28 [10.1109/ICPR.2014.175].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1060473
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